A Mayr1, H Binder, O Gefeller, M Schmid. 1. Andreas Mayr, Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Waldstr. 6, 91054 Erlangen, Germany, E-mail: andreas.mayr@fau.de.
Abstract
BACKGROUND: Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade. OBJECTIVES: This review highlights recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research. METHODS: We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed separately in the literature up to now. RESULTS: The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: i) efforts to ensure variable selection leading to sparser models, ii) developments regarding different types of predictor effects and how to choose them, iii) approaches to extend the statistical boosting framework to new regression settings. CONCLUSIONS: Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any regression setting in combination with a large amount of different types of predictor effects.
BACKGROUND: Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade. OBJECTIVES: This review highlights recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research. METHODS: We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed separately in the literature up to now. RESULTS: The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: i) efforts to ensure variable selection leading to sparser models, ii) developments regarding different types of predictor effects and how to choose them, iii) approaches to extend the statistical boosting framework to new regression settings. CONCLUSIONS: Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any regression setting in combination with a large amount of different types of predictor effects.
Authors: Paulino José García Nieto; Esperanza García-Gonzalo; Fernando Sánchez Lasheras; José Ramón Alonso Fernández; Cristina Díaz Muñiz; Francisco Javier de Cos Juez Journal: Environ Sci Pollut Res Int Date: 2018-05-30 Impact factor: 4.223
Authors: Andreas Mayr; Benjamin Hofner; Elisabeth Waldmann; Tobias Hepp; Sebastian Meyer; Olaf Gefeller Journal: Comput Math Methods Med Date: 2017-08-02 Impact factor: 2.238
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